Determining whether a target object was present during a scanning operation

ABSTRACT

Methods, devices and systems for determining whether an object is detected by a scanner are disclosed. Fast-Fourier-Transforms (“FFT”) are determined, modified and evaluated. Each FFT may be determined with regard to a subset of gain-compensated pixel-values corresponding to pixel-values of an acquired image.

CROSS-REFERENCE TO RELATED APPLICATION

This application claims the benefit of priority to U.S. provisionalpatent application Ser. No. 62/050,025, filed on Sep. 12, 2014.

FIELD OF THE INVENTION

The present invention relates to devices, systems, and methods ofdetermining whether an object has been presented for scanning.

BACKGROUND OF THE INVENTION

When using a scanning system, such as an ultrasonic scanner, it may bebeneficial to determine quickly whether an object has been scanned by ascanning operation. If an object was not present during the scanningoperation, then the information produced by the scanning operationshould be discarded and/or ignored without further processing so as topreserve processor availability and conserve energy. However, if anobject was present during the scanning operation, then information aboutthat object obtained during the scanning operation may be processed, forexample as part of an effort to authenticate an individual thatpresented the object. If authentication is made, then the individual maybe permitted to engage in certain activities. Such activities mayinclude, for example, the use of a smart phone or access to a restrictedarea within a building.

Methodologies for determining whether an object was present during ascanning operation have centered on determining a standard deviation forpixel information obtained from a scanning operation. Such methodologieshave proved to be inaccurate, particularly when the scanner capabilityhas degraded, which often occurs over time. Such inaccuracies may arisefrom the fact that a degraded sensor often produces complex patterns ornoisy images, either of which can be mistaken for an object, when infact no object has been presented for scanning.

SUMMARY

Described herein are methods for determining whether an object isdetected by a scanner. One such method begins by providing a scanner,such as an ultrasonic scanner having an area-array that includes aplurality of scanning elements for detecting ultrasonic energy. Thescanner is used to execute scanning operations in order to acquire atleast one information set (the “acquired information set”). The acquiredinformation set may be comprised of particular information valuescorresponding to pixels of the scanning area. For example, a particularpixel may correspond to one or more ultrasound transducers of thescanner. The acquired information set (e.g. the pixel values) may beprocessed to provide a processed information set. Processing of theacquired information set may include determining gain-compensatedpixel-values. Each gain-compensated pixel-value may correspond to apixel-value in the acquired information set.

Subsets of the processed information set may be identified, and aFast-Fourier-Transform (“FFT”) for each subset may be determined. Eachsubset may correspond to a different row or a different column of thescanning elements for detecting energy. Each FFT may be evaluated inorder to provide at least one FFT-derived output for each subset. Then,a determination may be made for each of the FFT-derived outputsregarding whether that subset indicates that a target object was presentduring the scan. An indicator may be provided for each subset indicatingwhether the FFT-derived outputs indicate that a target object waspresent during the scanning operation. Indicators indicating that anobject was not present during the scanning operation may be counted toobtain a count-value, and the count-value may be compared to acount-threshold. If the count-value exceeds the count-threshold, amessage may be sent to indicate that a target object was not presentduring the scanning operation.

Evaluation of an FFT may include removal of a DC component to provide amodified FFT (“MFFT”). The MFFT may be used in additional evaluatingefforts. For example, evaluation of an FFT may include identifying peaksof the MFFT. Identifying an MFFT peak may include identifying a maximumvalue of the peak and/or identifying a location of an MFFT peak.

Evaluation of an FFT may include counting peaks of the MFFT to provide apeak-count value. If the peak-count value exceeds apeak-number-threshold, then it may be assumed that the acquiredinformation set was too noisy, and it may be assumed that a targetobject was not present during the scanning operation. In such asituation, the indicator may indicate that a target object was notpresent during the scanning operation.

Evaluation of an FFT may include determining whether an identified peakof the MFFT is at a predetermined location. If the MFFT has anidentified peak at the predetermined location, and the peak-count valueis in a predetermined range, then the indicator may indicate that atarget object was not present during the scanning operation.

Evaluation of an FFT may include determining a peak ratio, which may bedetermined by (a) identifying a maximum value of a first peak, (b)identifying a maximum value of a second peak, and (c) dividing one ofthe maximum values by the other maximum value to produce the peak ratio.In addition, a standard deviation of the processed information set maybe determined. Then, the peak ratio may be compared to a PR-threshold,and a determination may be made as to whether the peak ratio exceeds thePR-threshold, and the calculated standard deviation may be compared to alocal SD-threshold, and a determination may be made as to whether thestandard deviation exceeds the local SD-threshold. If the standarddeviation does not exceed the local SD-threshold and the peak ratioexceeds the PR-threshold, then the indicator may indicate that a targetobject was not present during the scanning operation.

Also described herein are systems for determining whether an object isdetected. One such system may include a means for scanning, such as ascanner (e.g. an area-array ultrasonic scanner) and a means forprocessing, such as a processor (e.g. as a microprocessor). The scannermay have scanning elements arranged in rows and columns, and each subsetmay correspond to a different row or a different column of the scanningelements.

The scanner and processor are in communication with each other. Theprocessor is programmed to execute instructions for (a) executingscanning operations to acquire at least one information set (“acquiredinformation set”), (b) processing the acquired information set toprovide a processed information set, (c) identifying subsets of theprocessed information set, (d) determining a Fast-Fourier-Transform(“FFT”) for each subset, (e) evaluating each FFT to provide at least oneFFT-derived output for each subset, (f) determining whether theFFT-derived outputs for each subset indicate that a target object waspresent during the scan, and (g) for each subset, providing an indicatorindicating whether the FFT-derived outputs indicate that a target objectwas present during the scanning operation.

The processor may be programmed to execute instructions for (i) countingthe indicators indicating that a target object was not present duringscanning to provide a count-value, (ii) comparing the count-value to acount-threshold, and (iii) if the count-value exceeds thecount-threshold, sending a message indicating that a target object wasnot present during scanning. Such instructions may be fashioned to carryout one or more of the methods described herein.

Also described herein are non-transitory, computer-readable storagemedia having one or more computer programs of computer readableinstructions for execution by one or more processors that are incommunication with a biometric scanner to perform a method of generatingan image. Such computer program(s) may include instructions for:

-   -   (a) processing the acquired information set to provide a        processed information set;    -   (b) identifying subsets of the processed information set;    -   (c) determining a Fast-Fourier-Transform (“FFT”) for each        subset;    -   (d) evaluating each FFT to provide at least one FFT-derived        output for each subset;    -   (e) determining whether the FFT-derived outputs for each subset        indicate that a target object was present during the scan; and    -   (f) for each subset, providing an indicator indicating whether        the FFT-derived outputs indicate that a target object was        present during the scanning operation.

BRIEF DESCRIPTION OF THE DRAWINGS

For a fuller understanding of the nature and objects of the invention,reference should be made to the accompanying drawings and the subsequentdescription. Briefly, the drawings are:

FIG. 1 is a flow diagram depicting a method of determining whether atarget object was present during a scanning operation;

FIG. 2 shows 190 pixels, eight of which are identified as having beenselected (“S”);

FIG. 3 is a flow diagram depicting a method of determining FFT-derivedoutputs;

FIG. 4A is a graphical depiction of image-information obtained in afirst case study by an ultrasonic scanner during a scanning operation inwhich a target object was present on a platen;

FIG. 4B is an FFT magnitude plot corresponding to GCP-Values of thefirst case study;

FIG. 4C is a magnitude plot of an MFFT of the first case study;

FIG. 5A is a graphical depiction of image-information obtained in asecond case study by an ultrasonic scanner during a scanning operationin which a target object was not present on a platen;

FIG. 5B is an FFT magnitude plot corresponding to GCP-Values of thesecond case study;

FIG. 5C is a magnitude plot of an MFFT of the second case study;

FIG. 6A is a graphical depiction of image-information obtained in athird case study by an ultrasonic scanner during a scanning operation inwhich a target object was not present on a platen;

FIG. 6B is an FFT magnitude plot corresponding to GCP-Values of thethird case study;

FIG. 6C is a magnitude plot of an MFFT of the third case study;

FIG. 7 schematically depicts a system for determining whether an objectwas present during a scanning operation; and

FIGS. 8A, 8B and 8C are schematics of a system at various stages of ascanning operation involving a finger.

FURTHER DESCRIPTION OF THE INVENTION

Described below are devices, systems and methods for determining whetheran object was present or absent during a scanning operation. Thescanning operation may be carried out by a scanner that obtainsinformation about the object by detecting energy reflected from theobject, or by detecting a change in energy caused by the presence ofthis object. Such systems may employ capacitive, radio-frequency,thermal, piezo-resistive, ultrasonic, or piezoelectric sensors as themeans for detecting the presence of an object. Many such scanningdevices exist for detecting ultrasound or light reflected from anobject, such as a finger. One such ultrasonic system is QualcommIncorporated's 3D Fingerprint sensor, that is based on ultrasonictechnology. A system for detecting a change in energy may be based on acapacitor arrangement, such as that utilized by model SmartFinger® 500dpi capacitive fingerprint swipe sensors, provided by joint effortbetween Idex ASA, a Norwegian company specializing in fingerprintrecognition (Idex), STMicroelectronics in France (ST) and SINTEF aNorwegian research institute (SINTEF). For ease of describing, we referto a scanner that uses ultrasound to scan a finger for purposes ofauthenticating an individual, but the devices, systems and methodsdescribed herein are not limited to use with an ultrasoundauthentication system.

A scanning operation may be carried out using an array of ultrasonicsensors in order to acquire information (sometimes referred to herein as“image-information”). That image-information may describe an object,such as the friction ridge surface (e.g. fingerprint) of a finger thatmay reside on a platen of the scanner. It should be noted that themethodologies described herein are not limited to use with a scannerhaving a platen, and may be employed using a scanner that does not havea platen. However, since many scanners have a platen, embodiments of theinvention are described herein with reference to a platen. The acquiredimage-information resulting from the scanning operation may be processedand analyzed to determine whether the acquired image-information hascharacteristics that indicate whether an object was provided during thescanning operation.

FIG. 1 is a flow diagram describing a method of determining whether anobject (sometimes referred to herein as a “target object”) was providedduring a scanning operation. FIG. 1 notes that the scanner is caused toexecute 10 a scan in order to obtain image-information, which iscomprised of a plurality of pixel-values, describing an object that thisin close proximity to an array of ultrasonic sensors. Each pixel-valuemay be indicative of the amount of ultrasonic energy reflected from thesurface of the platen on which a target object would reside, if a targetobject were provided for scanning. The pixel-values may be numberscorresponding to a gray-scale. For example, areas where little or noultrasound energy was reflected may have a pixel-value corresponding tothe darker end of the gray-scale spectrum, while areas where much of theultrasound energy was reflected may have a pixel-value corresponding tothe lighter end of the gray-scale spectrum. Each pixel-value may beprocessed to provide a corresponding set of processed information. Forexample, each pixel-value may be processed to provide a gain-compensatedpixel-value (sometimes referred to herein as “GCP-Value”). The processedinformation set (e.g. gain-compensated pixel-values) may be used todetermine whether a target object was present during the scanningoperation. It should be noted that the invention is described hereinwith reference to GCP-Values, but other types of processed information(e.g. those arising from the use of normalization transforms) may beused. For example, see M. Gruber and K. Y. Hsu, “Moment-Based ImageNormalization with High Noise Tolerance,” IEEE Transactions on PatternAnalysis and Machine Intelligence, vol. 19, pp. 136-139, 1997. Also, alinear normalization procedure for a gray scale input image is describedin K. Jain, “Fundamentals of Digital Image Processing,” EnglewoodCliffs, N.J.: Prentice Hall, 1989, and R. C. Gonzalez, R. E. Woods, S.L. Eddins, “Digital Image Processing using Matlab, 2nd edition,”Pearson-Prentice-Hall, 2009.

In one such method for determining whether a target object was presentduring a scanning operation, a count-value is set to a base value, suchas zero, and the count-value is incremented 60 each time one or morecriteria are met. For example, there may be three criteria, and if oneof these criteria is met, then the count-value may be incremented 60.Once the gain-compensated pixel values are processed to determinewhether the criteria have been met, the count-value may be compared 80to a count-threshold, and if the count-value exceeds thecount-threshold, then it may be determined that no target object waspresent during the scanning operation, and a message may be sentindicating the absence of a target object. Upon receipt of such amessage, the image-information obtained by that scanning operation maybe discarded.

However, if the count-value does not exceed the count-threshold, then itmay be determined that a target object was present during the scanningoperation, and a message may be sent indicating the presence of a targetobject. Upon receipt of such a message, the image-information obtainedby that scanning operation may be saved and/or processed further. Asnoted above, if it is determined that a target object was present duringthe scanning operation, then the image-information may be processed aspart of an effort to authenticate the individual that presented thetarget object. If authenticated, that individual may be permitted toengage in an activity, such as using a smart phone or entering abuilding.

The method depicted in FIG. 1 identifies three criteria. A first of thecriteria is met if an SD-Value of gain-compensated pixel-values is belowan SD-threshold value, and a PR-Output is above a PR-threshold value. Asecond of the criteria is met if a PN-Output is above a PN-thresholdvalue. A third of the criteria is met if a PL-Output is in apredetermined PL-range, and the PN-Output is in a predeterminedPN-range. Each of these criteria is explained in greater detail below.

GCP-Values: The criteria outlined in the preceding paragraph operate ongain-compensated pixel-values. There are many methods of obtaininggain-compensated pixel-values. For example, see M Tang, D C Liu,“Rationalized gain compensation for ultrasound imaging”, 7thAsian-Pacific Conference on Medical and Biological Engineering, 282-285,Lee, Duhgoon; Kim, Yong Sun; Ra, Jong Beom, “Automatic time gaincompensation and dynamic range control in ultrasound imaging systems”,Medical Imaging 2006: Ultrasonic Imaging and Signal Processing. Editedby Emelianov, Stanislav; Walker, William F. Proceedings of the SPIE,Volume 6147, pp. 68-76 (2006), and Mackovski, A. (1983) Medical ImagingSystems. Prentice-Hall, Englewood Cliffs, N.J. That being the case, wesummarize one manner of generating the GCP-Values, which involvesacquiring five images. However, it should be noted that devices,systems, and methods of determining the presence of an object need notutilize this particular manner of obtaining gain-compensatedpixel-values.

A particular manner of obtaining gain-compensated pixel-values may usean area-array ultrasonic scanner. The area-array ultrasonic scanner hasa plurality of ultrasound detectors, each of which generates a pixel ofinformation comprising the image-information. Each pixel of informationmay be indicative of the amount of energy received by a particularultrasound detector. To generate gain-compensated pixel-values from thepixel-values comprising the image-information, five scanning operationsmay be made, each of which will produce a set of image-information.Those five sets of image-information may be acquired as follows:

-   -   Image #1: Place a target object (“TO”) on the scanner, and        collect image-information during a scanning operation with the        tone burst of the scanner turned off. We refer to this first set        of image-information as “TO_(off)”.    -   Image #2: With the target object still on the scanner, collect        image-information during a scanning operation with the tone        burst of the scanner turned on. We refer to this second set of        image-information as “TO_(on)”.    -   Image #3: Collect image-information during a scanning operation        with the tone burst of the scanner turned off when no target        object (“NO”) is presented to the scanner. We refer to this        third set of image-information as “NO_(off)”.    -   Image #4: Collect image-information during a scanning operation        with the tone burst of the scanner turned on when no target        object is presented to the scanner. We refer to this fourth set        of image-information as “NO_(on)”.    -   Image #5: Collect image-information during a scanning operation        with the tone burst of the scanner turned off and the Dbias set        at +0.1V when no target object is presented to the scanner. We        refer to this fifth set of image-information as “NOdb”.        Images #3, #4, and #5 may be obtained prior to an operation in        which it is desired to know whether a target object is present.        For example, Images #3, #4, and #5 may be obtained as part of a        final step of the manufacturing process, or may be obtained as        part of procedures for installing the scanner. Images #1 and #2        may be obtained as part of an effort to determine whether a        target object is present, which itself may be part of an effort        to authenticate an individual.

Using those five images, and processing on a pixel-by-pixel basis, thegain-compensated value for each pixel may be generated using thefollowing equation:

${{GCP}\text{-}{Value}} = \frac{\left( {{TO}_{on} - {NO}_{on}} \right) - \left( {{TO}_{off} - {NO}_{off}} \right)}{{{{NO}_{on} - {NO}_{off}}} + {0.3{{{NO}_{db} - {NO}_{off}}}}}$

Having provided information about gain-compensated pixel-values, we nowprovide information useful in understanding elements of the threecriteria outlined above. Specifically, we provide descriptions of how anSD-Value and three FFT-derived outputs (PR-Output, PN-Output, andPL-Output) may be obtained.

SD-Values: The standard deviation of the GCP-Values (the “overallSD-Value”) may be determined 20. The overall standard deviation may beprovided, and used for purposes of determining whether the overall imageis too noisy. For example, the overall SD-Value may be compared to athreshold value, and if the overall SD-Value exceeds that threshold,then the acquired information set may be deemed too noisy to processfurther.

In addition, an SD-Value may be determined for a selected subset (the“local SD-Value”) of the processed information set (e.g. the GCP-Values)and later used for purposes of determining whether the count-valueshould be incremented. In particular, the local SD-Value may be used inconjunction with a PR-Output to determine whether to increment thecount-value. Selection of a subset is discussed below in more detail.

FFT Derived Output: A PR-Output, PN-Output, and PL-Output may be derivedfor each of a subset of the data that comprise the GCP-Values. Forexample, a row (or column) of pixels may be selected, and the GCP-Valuesfor that row (or column) of pixels may be identified 30 to comprise asubset from which a Fast-Fourier-Transform (“FFT”) is derived. Computersmay be programmed to receive values, such as a subset of the GCP-Values,and produce 40 an FFT corresponding to those values. A particularprogram that may be used for such purposes is FastCV Computer Vision SDKdeveloped by Qualcomm Technologies Inc.(https://developer.qualcomm.com/docs/fastcv/api/index.html).

More generally, the subset of the data that comprise the selectedGCP-Values may be selected to include the GCP-Values corresponding to agroup of pixels, which may be a column or row of pixels corresponding toa column or row of the scanner. However, the group of pixels need notcorrespond to a column or row of the scanner. For example, the group ofpixels may be a block of pixels, for which each pixel in the block isimmediately adjacent to at least one other pixel in the block, orimmediately adjacent to at least two other pixels in the block. Toillustrate, FIG. 2 shows 190 pixels from a scanning area, eight of whichhave been selected (“S”) to comprise a group. It should be noted thatthe group of pixels need not be a straight line (e.g. row or column) ora block: other shapes (e.g. a zig-zag line) or a non-standard shape maybe used to identify the group of pixels. In addition, the group ofpixels need not be contiguous. For example, it may be the case thatseveral non-contiguous groups of contiguous pixels may be selected, andthe GCP-Values used to produce the FFT.

The group of pixels may be all of the scanning area, or a subset of thescanning area. In addition, the selected group of pixels may initiallycorrespond to one group of pixels, and then, later a different group ofpixels from the same set of acquired information set. For clarity, themethodologies may be applied multiple times using the same set ofacquired information, each time analyzing a different portion of theacquired information. A subsequently selected group of pixels may be asubset of an initially selected pixel group, or may include some pixelsthat were, and some that were not, part of an initially selected pixelgroup. Or the subsequently selected group of pixels may be an entirelydifferent group of pixels from those that were initially selected.

By applying the methodologies described herein multiple times usingsubsets of the information from one acquired information set, a searchfor an object (such as a fingerprint or stylus) may be carried out. Assuch, in a situation in which some portions of the scanning area have noobject, it may nevertheless be possible to identify one portion thatdoes have an object presented. With this information, it may be possibleto determine which portions of the acquired information containinformation about an object, and which do not. As such, it may bepossible to determine the percentage of pixels that produced informationabout an object, or conversely the percentage of pixels which did not.

In addition, if a scanning area has a small portion in which an objectis presented, it may be the case that analyzing the entire scanning area(or some large portion of the scanning area) will produce a conclusionthat no object was presented simply because those pixels producingcharacteristics consistent with an object are not as numerous as thosepixels producing characteristics consistent with no object beingpresented. By applying the methodologies to different portions of anacquired information set, it may be possible to find those pixels thatare producing characteristics consistent with an object, and therebycorrectly conclude that an object was presented. In addition, it may bepossible to identify those pixel-areas of a scanner which produce alarge amount of noise, and thereby exclude them from influencing aneffort to determine whether an object is presented to a scanner, forexample by excluding information produced by those pixels from beingused to determine the SD-Values, PL-Output, PR-Output, and/or PN-Output.Or, it may be possible to identify those pixel-areas of a scanner whichare producing a large amount of noise, and then establish criteria (e.g.predetermined peak location, PR-threshold, PN-threshold, PN-range) forthat area that is different from other areas of the scanner.

Ideally, each subset of the GCP-Values is selected to be comprisedprimarily of GCP-Values derived from pixels which will detect a targetobject, if a target object is presented for scanning. If the scanningarea is normally completely covered by a target object, most or all ofthe pixels may produce potential candidates for selection 40 as a subsetfrom which an FFT is derived. This may be the case where the scanner ispart of a cell phone, and the target object is a finger presented forpurposes of authenticating the user as an authorized user of the cellphone. In such situations, the scanner is normally completely covered bythe user's finger during an authentication process.

However, if the scanning area is larger than the expected size of thetarget object, there may be pixels that often do not detect a targetobject when a target object is presented, and those pixels may notproduce ideal candidates for selection as a subset from which an FFT isderived. In those situations, it may be that some subsets, for examplethe columns or rows at the edges of the scanning area, will not likelyproduce ideal candidates for selection into a subset, but that othersubsets (e.g. columns or rows that include pixels in the middle of thescanning area) are more likely to produce good candidates that will(when analyzed) indicate the presence of an object when an object ispresented, and indicate the absence of an object when an object is notpresented.

Once a subset of the GCP-Values is selected 40, the FFT for that subsetmay then be determined 40 and analyzed to determine 50 the PR-Output,PN-Output, and PL-Output (collectively, the “FFT-derived outputs”) forthat subset. As noted above, the FFT-derived outputs may be used todetermine whether to increment 60 the count-value, which is used todetermine whether a target object was present when Images #1 and #2 wereobtained. This process may be repeated 70 if there are additionalsubsets to analyze.

FIG. 3 depicts steps of a method for obtaining the FFT-derived outputsfor a particular subset. Once the FFT is obtained 200, a DC component ofthe FFT may be removed 210 in order to provide a modified FFT (the“MFFT”). Peaks of the MFFT may be identified 220 and used in threedifferent operations, each of which produces one of the PR-Output,PN-Output, or PL-Output.

To produce the PL-Output, the location of peaks of the MFFT aredetermined and compared to a predetermined location. The predeterminedlocation may be a location on a plot of the MFFT that is known (e.g.from prior experience) to have a peak when no target object is presentfor scanning. To afford some flexibility, the predetermined location maybe defined as a range, and if the location of one of the peaks of theMFFT falls within that PL-range, a determination 230 may be made thatthe PL-Output should indicate the presence of a peak at thepredetermined location. However, if none of the peaks of the MFFT fallswithin that PL-range, a determination 230 may be made that the PL-Outputshould indicate that no peak was present at the predetermined location.

To produce the PR-Output, two peaks of the MFFT may be selected 240, anda ratio of the corresponding peak values generated 250. For example, themaximum values of the two tallest peaks may be identified, and thepeak-ratio may be determined by dividing the value of the tallest peakby the value of the next-tallest peak. That determined peak-ratio may becompared to a PR-threshold value, and if the ratio exceeds thePR-threshold value, then a determination 260 may be made that thePR-Output should indicate a high peak-ratio. However, if the peak-ratiodoes not exceed the PR-threshold value, then a determination 260 may bemade that the PR-Output should indicate that there is not a highpeak-ratio. The PR-threshold value used for determining whether a highpeak-ratio exists for the MFFT may be obtained by peak detectiontechniques described in the following publications: S. J. Davey, S. B.Colegrove, and D. Mudge, 1999, “Advanced Jindalee Tracker: Enhanced PeakDetector”, DSTO Australia, Technical Report No. DSTO-TR-0659, and alsoJiapu Pan and Willis J. Tompkins, “A Real-Time QRS Detection Algorithm”,IEEE Transactions on Biomedical Engineering, Vol. BME-32, No. 3, March1985.

From the description above, it will now be recognized that the criteriaare applied to each of the subsets. As such, each subset is producing anindication as to whether that subset indicates the presence or absenceof a target object during a scanning operation. If a particular subsetindicates that a target object was not present during a scanningoperation, then the count-value is incremented. But, if a particularsubset indicates that a target object was present during a scanningoperation, then the count-value is not incremented. By setting thecount-threshold, a system manager may establish the degree to which thesystem will indicate the presence or absence of a target object. Forexample, if the system manager desires a high degree of certainty that atarget object is present, the system manager may set the count-thresholdat a value which is low. Or, if the system manager desires a low degreeof certainty that a target object is present, the system manager may setthe count-threshold at a value which is high. Similarly, the localSD-threshold value, PR-threshold value, PN-threshold value, PL-range,and/or PN-range may be adjusted to obtain a desired degree of certainty.

To produce the PN-Output, peaks of the MFFT that exceed a predeterminedPN-threshold value may be identified and counted 270. For example, thepredetermined PN-threshold value may be set at a percentage (e.g. 8%) ofthe value corresponding to one of the peaks, for example the tallest orsecond-tallest peak, of the MFFT. So, for example, if the second-tallestpeak of the MFFT has a value of 1300 and the set percentage is 9%, thenpeaks having a value above 117 would be counted, while peaks having avalue at or below 117 would not be counted. If the number of peakscounted exceeds the PN-threshold value, then a determination 280 may bemade that the PN-Output should indicate the presence of a high number ofpeaks. If the counted number of peaks does not exceed the PN-thresholdvalue, a determination 280 may be made that the PN-Output shouldindicate that there is not a high number of peaks in the MFFT.Techniques for determining the PN-threshold value used for determining280 whether a high number of peaks in an MFFT exists are described inthe following article: Jiapu Pan and Willis J. Tompkins, “A Real-TimeQRS Detection Algorithm”, IEEE Transactions on Biomedical Engineering,Vol. BME-32, No. 3, March 1985.

In addition, if the number of peaks counted is in a predetermined range,then a determination 290 may be made that the PN-Output should indicatethat the number of counted peaks is within the predetermined PN-range.However, if the number of counted peaks of the MFFT is not in thepredetermined PN-range, a determination 290 may be made that thePN-Output should indicate that the number of counted peaks is not withinthe predetermined range.

Case Studies: The following three case studies illustrate aspects of themethodology described above.

FIGS. 4A, 4B, and 4C correspond to a first of the case studies. FIG. 4Agraphically illustrates image-information obtained by an ultrasonicscanner during a scanning operation in which a target object was presenton a platen. The image-information is provided in FIG. 4A using agray-scale. Ridges of the fingerprint appear as dark areas, and valleysof the fingerprint appear as light areas.

FIG. 4B is an FFT magnitude plot corresponding to GCP-Values of thefirst case study. The large peak in the middle of the FFT plotcorresponds to the DC component. By removing the DC component andadjusting the values identified on the axes of the plot, FIG. 4Cresults, which is a magnitude plot of an MFFT of the first case study.It should be noted that the MFFT plot of FIG. 4C shows a large number ofpeaks, which is in keeping with the presence of a target object, and thecounted number of peaks did not exceed a PN-threshold. This particularcase study did not produce tall peaks in predetermined locationscorresponding to an absence of a target object. In addition, this firstcase study produced a local standard deviation exceeding a localSD-threshold. As such, the methodology described above produced anindication that a target object was presented during the scanningoperation.

FIGS. 5A, 5B, and 5C correspond to a second of the case studies. FIG. 5Agraphically illustrates image-information obtained by the ultrasonicscanner during a scanning operation in which a target object was notpresent on a platen. The image-information is provided in FIG. 5A usinga gray-scale. Unlike FIG. 4A, the image of FIG. 5A is relativelyuniform, and therefore indicates the absence of a target object.

FIG. 5B is an FFT magnitude plot corresponding to GCP-Values of thesecond case study. The large peak in the middle of the FFT plotcorresponds to the DC component. By removing the DC component andadjusting the values identified on the axes of the plot, FIG. 5Cresults, which is a magnitude plot of an MFFT of the second case study.It should be noted that the MFFT plot of FIG. 5C shows a large number ofpeaks, but most are not very tall, which is in keeping with the absenceof a target object. In this second case study, the counted number ofpeaks did not exceed a PN-threshold. Also, FIG. 5C shows tall peaks ofthe MFFT in each of two predetermined locations, which are identified bycircles. This second case study produced an indication that a targetobject was not present during the scanning operation.

FIGS. 6A, 6B, and 6C correspond to a third of the case studies. FIG. 6Agraphically illustrates image-information obtained by the ultrasonicscanner during a scanning operation in which a target object was notpresent on a platen. Unlike the second case study, the third case studyproduced a very noisy image. See FIG. 6A and FIG. 6C. To improve theability to distinguish between images that are merely noisy, and imageshaving noise but which may include an object, the criteria used toidentify the presence of an object may be adjusted based on thetemperature of the scanner 300. See FIG. 7. In such an embodiment, atemperature sensor 340 may be included, and used by the microprocessor310 to select (a) values for (a) the predetermined location used todecide the PL-Output, and/or (b) the PR-threshold, and/or (c) thePN-threshold and/or (d) the PN-range that are previously determined tobe best for that particular temperature.

FIG. 6B is an FFT magnitude plot corresponding to GCP-Values of thethird case study. The large peak in the middle of the FFT plotcorresponds to the DC component. By removing the DC component andadjusting the values identified on the axes of the plot, FIG. 6Cresults, which is a magnitude plot of an MFFT of the third case study.It should be noted that, unlike FIG. 5C, the MFFT shown in FIG. 6C showsa large number of very tall peaks. In this third case study, the countednumber of peaks exceed a PN-threshold, and so the PN-Output indicated ahigh number of peaks. The resulting indication for case study three wasthat a target object was not present during the scanning operation.

Having described methods for determining whether an object is detectedby a scanning operation, it should be apparent that a system forperforming those methods may include a scanner 300 and a processor 310.See FIG. 7. The processor 310 may be in communication with the scanner300 via a communication channel 320, and programmed to executeinstructions. The instructions may be stored on a non-transitory,computer-readable storage medium 330, such as a CD, USB-flash drive, orread-only-memory device. A temperature sensor 340 may be used todetermine the temperature of the scanner 300, and provide temperatureinformation to the microprocessor 310. The temperature information maybe used by the microprocessor to adjust criteria used to determinewhether an object is presented to the scanner 300.

The scanner 300 may have an area-array of scanning elements, at leastsome of which detect energy, such as ultrasound energy. The area-arraymay be arranged so that the scanning elements are arranged in rows andcolumns.

The processor 310 may be programmed to execute instructions for:

-   -   executing scanning operations to acquire at least one        information set (“acquired information set”);    -   processing the acquired information set to provide a processed        information se, which may include gain-compensated pixel values;    -   identifying subsets of the processed information set;    -   determining a Fast-Fourier-Transform (“FFT”) for each subset;    -   evaluating each FFT to provide at least one FFT-derived output        for each subset;    -   determining whether the FFT-derived outputs for each subset        indicate that a target object was present during the scan;    -   for each subset, providing an indicator indicating whether the        FFT-derived outputs indicate that a target object was present        during the scanning operation.

The processor 310 may be programmed to execute instructions for:

-   -   counting the indicators indicating that a target object was not        present during scanning to provide a count-value;    -   comparing the count-value to a count-threshold; and    -   if the count-value exceeds the count-threshold, sending a        message indicating that a target object was not present during        scanning.

The instructions for evaluating each FFT may include instructions for:

-   -   (a) removing a DC component of the FFT in order to provide a        modified FFT (“MFFT”),    -   (b) identifying peaks of the MFFT,    -   (c) counting peaks of the MFFT to provide a peak-count value;    -   (d) determining whether an identified peak of the MFFT is at a        predetermined location;    -   (e) determining a peak ratio, the peak ratio is determined by:        -   identifying a maximum value of a first peak;        -   identifying a maximum value of a second peak;        -   dividing one of the maximum values by the other maximum            value to produce the peak ratio.

FIGS. 8A, 8B and 8C depict a scanner 300 at various stages of a scanningoperation. The processor 310 may be in communication with an energytransmitter 420, such as an ultrasound generator, and an energy receiver430, such as an ultrasound receiver. The processor 310 may be programmedto cause the scanner 300 to scan the object 470 residing on a platen440, and to receive information from the receiver 430 about reflectedenergy detected by the receiver 430. Programming of the processor 310may be via a non-transitory, computer-readable storage medium 330, suchas a CD, USB-flash drive, or read-only-memory device. A scan may includecausing the transmitter 420 to produce a plane-wave 480 a travelingtoward the object 470, such as a finger, that resides on an exposedsurface 444 of the platen 440. An outer surface of the object 470 mayhave the ridges 472 and valleys 474, such as those commonly found on thefriction ridge of a finger.

The emitted plane-wave 480 a travels to the object 470, where some ofthe wave energy is reflected. The reflected energy 480 b then travels tothe receiver 430 where the reflected energy 480 b is detected by thereceiver 430. The processor 310 may be programmed to analyze reflectedenergy 480 b that has been detected by the receiver 430, and thenprovide an analysis result. Analysis of the reflected energy 480 b maybe according to methods described above. The analysis result may be an“object” flag (if an objected is determined to be present) or an “air”flag (if no object is determined to be present).

Although the present invention has been described with respect to one ormore particular embodiments, it will be understood that otherembodiments of the present invention may be made without departing fromthe spirit and scope of the present invention. Hence, the presentinvention is deemed limited only by the appended claims and thereasonable interpretation thereof.

What is claimed is:
 1. A method for determining whether an object isdetected by a scanner, comprising: providing a scanner; executingscanning operations using the scanner to acquire at least oneinformation set (“acquired information set”); processing the acquiredinformation set to provide a processed information set; identifyingsubsets of the processed information set; determining aFast-Fourier-Transform (“FFT”) for each subset; evaluating each FFT toprovide at least one FFT-derived output for each subset; determiningwhether the FFT-derived outputs for each subset indicate that a targetobject was present during the scan; and for each subset, providing anindicator indicating whether the FFT-derived outputs indicate that atarget object was present during the scanning operation.
 2. The methodof claim 1, further comprising: counting the indicators indicating thata target object was not present during scanning to provide acount-value; comparing the count-value to a count-threshold; and if thecount-value exceeds the count-threshold, sending a message indicatingthat a target object was not present during scanning.
 3. The method ofclaim 1, wherein the scanner is an area-array ultrasonic scanner.
 4. Themethod of claim 1, wherein evaluating each FFT includes removal of a DCcomponent to provide a modified FFT (“MFFT”).
 5. The method of claim 4,wherein evaluating each FFT includes identifying peaks of the MFFT. 6.The method of claim 5, wherein evaluating each FFT includes countingpeaks of the MFFT to provide a peak-count value.
 7. The method of claim6, wherein if the peak-count value exceeds a peak-number-threshold, thenthe indicator indicates that a target object was not present during thescanning operation.
 8. The method of claim 6, wherein evaluating eachFFT includes determining whether an identified peak of the MFFT is at apredetermined location.
 9. The method of claim 8, wherein if the MFFThas an identified peak at the predetermined location, and the peak-countvalue is in a predetermined range, then the indicator indicates that atarget object was not present during the scanning operation.
 10. Themethod of claim 5, wherein evaluating each FFT includes determining apeak ratio, the peak ratio is determined by: identifying a maximum valueof a first peak; identifying a maximum value of a second peak; dividingone of the maximum values by the other maximum value to produce the peakratio.
 11. The method of claim 10, further comprising calculating astandard deviation of the processed information set.
 12. The method ofclaim 11, further comprising: comparing the peak ratio to aPR-threshold; determining whether the peak ratio exceeds thePR-threshold; comparing the calculated standard deviation to a localSD-threshold; determining whether the standard deviation exceeds thelocal SD-threshold; if the standard deviation does not exceed the localSD-threshold and the peak ratio exceeds the PR-threshold, then theindicator indicates that a target object was not present during thescanning operation.
 13. A system for determining whether an object isdetected, comprising: a scanner; a processor in communication with thescanner, the processor being programmed to execute instructions for:executing scanning operations to acquire at least one information set(“acquired information set”); processing the acquired information set toprovide a processed information set; identifying subsets of theprocessed information set; determining a Fast-Fourier-Transform (“FFT”)for each subset; evaluating each FFT to provide at least one FFT-derivedoutput for each subset; determining whether the FFT-derived outputs foreach subset indicate that a target object was present during the scan;and for each subset, providing an indicator indicating whether theFFT-derived outputs indicate that a target object was present during thescanning operation.
 14. The system of claim 13, wherein the scanner isan area-array ultrasonic scanner, and the area-array has scanningelements arranged in rows and columns, and each subset corresponds to adifferent row or a different column of the scanning elements.
 15. Thesystem of claim 13, wherein processing the acquired information setincludes determining gain-compensated pixel-values.
 16. The system ofclaim 13, wherein evaluating each FFT includes removal of a DC componentto provide a modified FFT (“MFFT”).
 17. The system of claim 16, whereinevaluating each FFT includes identifying peaks of the MFFT.
 18. Thesystem of claim 17, wherein evaluating each FFT includes counting peaksof the MFFT to provide a peak-count value.
 19. The system of claim 18,wherein if the peak-count value exceeds a peak-number-threshold, thenthe indicator indicates that a target object was not present during thescanning operation.
 20. The system of claim 18, wherein evaluating eachFFT includes determining whether an identified peak of the MFFT is at apredetermined location.
 21. The system of claim 20, wherein if the MFFThas an identified peak at the predetermined location, and the peak-countvalue is in a predetermined range, then the indicator indicates that atarget object was not present during the scanning operation.
 22. Thesystem of claim 18, wherein evaluating each FFT includes determining apeak ratio, the peak ratio is determined by: identifying a maximum valueof a first peak; identifying a maximum value of a second peak; dividingone of the maximum values by the other maximum value to produce the peakratio.
 23. The system of claim 22, further comprising calculating astandard deviation of the processed information set.
 24. The system ofclaim 23, further comprising: comparing the peak ratio to aPR-threshold; determining whether the peak ratio exceeds thePR-threshold; comparing the calculated standard deviation to a localSD-threshold; determining whether the standard deviation exceeds thelocal SD-threshold; if the standard deviation does not exceed the localSD-threshold and the peak ratio exceeds the PR-threshold, then theindicator indicates that a target object was not present during thescanning operation.
 25. A non-transitory, computer-readable storagemedium comprising one or more computer programs of computer readableinstructions for execution by one or more processors in communicationwith a biometric scanner to perform a method of generating an image, thecomputer program(s) comprising instructions for: processing the acquiredinformation set to provide a processed information set; identifyingsubsets of the processed information set; determining aFast-Fourier-Transform (“FFT”) for each subset; evaluating each FFT toprovide at least one FFT-derived output for each subset; determiningwhether the FFT-derived outputs for each subset indicate that a targetobject was present during the scan; and for each subset, providing anindicator indicating whether the FFT-derived outputs indicate that atarget object was present during the scanning operation.
 26. The storagemedium of claim 25, wherein evaluating each FFT includes removal of a DCcomponent to provide a modified FFT (“MFFT”).
 27. The storage medium ofclaim 26, wherein evaluating each FFT includes identifying peaks of theMFFT.
 28. A system for determining whether an object is detected,comprising: a means for scanning; and a means for processing incommunication with the means for scanning, the means for processingbeing programmed to execute instructions for: executing scanningoperations to acquire at least one information set (“acquiredinformation set”); processing the acquired information set to provide aprocessed information set; identifying subsets of the processedinformation set; determining a Fast-Fourier-Transform (“FFT”) for eachsubset; evaluating each FFT to provide at least one FFT-derived outputfor each subset; determining whether the FFT-derived outputs for eachsubset indicate that a target object was present during the scan; andfor each subset, providing an indicator indicating whether theFFT-derived outputs indicate that a target object was present during thescanning operation.
 29. The system of claim 28, wherein evaluating eachFFT includes removal of a DC component to provide a modified FFT(“MFFT”).
 30. The system of claim 29, wherein evaluating each FFTincludes identifying peaks of the MFFT.